TISCOPE enables integrative and comparative analyses of spatial omics data to reveal condition-associated tissue modules
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Installation
- We recommend creating a virtual environment using Python 3.11:
conda create -n tiscope python=3.11
conda activate tiscope
- (Skip if PyTorch is already installed) install PyTorch following the PyTorch installation guide. For example, on a machine with
cuda 12.x:
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu126
- (Skip if PyG is already installed) Install PyG following the PyG installation guide, usually:
pip install torch_geometric
- Install dependencies:
pip install 'scanpy[leiden]' louvain squidpy ipykernel
- Install TISCOPE:
Via Pypi:
pip install tiscope
or git clone and install
git clone git://github.com/zhangqf-lab/TISCOPE.git
cd tiscope
pip install -e .
Getting started
Please refer to the Documentation.
Release notes
Citation
Release files for tiscope 0.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tiscope-0.0.2.tar.gz | 16.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tiscope-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 33.2 kB
Release files / tiscope-0.0.2.tar.gz
| Download URL | tiscope-0.0.2.tar.gz |
|---|---|
| Size | 16.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.9.6
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Release files / tiscope-0.0.2-py3-none-any.whl
| Download URL | tiscope-0.0.2-py3-none-any.whl |
|---|---|
| Size | 16.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.9.6
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